Machine learning network implemented by statically scheduled instructions, with mla chip

ABSTRACT

A compiler receives a description of a machine learning network and generates a computer program that implements the machine learning network. The computer program includes statically scheduled instructions that are executed by a mesh of processing elements (Tiles). The instructions executed by the Tiles are statically scheduled because the compiler can determine which instructions are executed by which Tiles at what times. For example, for the statically scheduled instructions, there are no conditions, branching or data dependencies that can be resolved only at run-time, and which would affect the timing and order of the execution of the instructions.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. patent application Ser. No.16/840,216, “Machine Learning Network Implemented by StaticallyScheduled Instructions, with Compiler,” filed Apr. 3, 2020. The subjectmatter of all of the foregoing is incorporated herein by reference intheir entirety.

BACKGROUND 1. Technical Field

This disclosure relates, in general, to the implementation of machinelearning networks on hardware.

2. Description of Related Art

Machine learning is one of the most powerful recent trends intechnology. In machine learning, a model is developed to perform acertain task. The model, which will be referred to as a machine learningnetwork, is trained and deployed in order to carry out that task. Forexample, a model may be developed to recognize the presence of objectswithin images captured by a set of cameras. Once the model is deployed,images captured by the cameras are input to the machine learningnetwork, which then outputs whether (or to what confidence level)objects are present within the images.

Machine learning networks typically require the handling of a largevolume of data and the execution of a large number of computations. As aresult, they are commonly implemented in compute facilities with accessto significant resources, such as in the cloud or on server clusters.However, the sources of input to machine learning networks may belocated remotely from these compute facilities. For example, cameras andother types of sensors may be located on the edge of the network.Example applications for edge devices include automotive and other formsof transportation including autonomous transportation, agricultural,industrial, robotics, drones, surveillance and security, smartenvironments including smart cities, medical, and personalized health.Example tasks include computer vision, image analysis, imageunderstanding, speech recognition, audio analysis, audio understanding,natural language processing, classification and pattern recognitiontasks. For edge devices, it may be desirable to perform certain tasks inreal-time. In addition to memory and other programmable processors, anedge device may also include sensors, such as cameras (both still imageand video cameras), microphones, temperature sensors, pressure sensorsand other types of sensors. The sensors may capture samples that areused as inputs to a computing pipeline within the edge device. Thus, onecommon paradigm is for the input sources to be web-based so that theycan continuously send their captured data to the cloud-based computefacility, which then executes the machine learning network and returnsthe result.

However, there can be many advantages if the machine learning networkand computing elements on which it executes was instead embedded on edgedevices, such as combined with the camera system.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure have other advantages and features whichwill be more readily apparent from the following detailed descriptionand the appended claims, when taken in conjunction with the examples inthe accompanying drawings, in which:

FIG. 1A is a block diagram of a system with a machine learningaccelerator (MLA) and corresponding compiler, according to theinvention.

FIG. 1B illustrates partitioning a computer program into deterministicand non-deterministic phases.

FIG. 2A is a block diagram of a hardware system, including an MLA.

FIG. 2B is a block diagram of a Tile within an MLA.

FIG. 3 is a block diagram of a software development environment,including an ML compiler.

FIG. 4A illustrates an implementation of an MLN subnet utilizing a lownumber of Tiles.

FIG. 4B illustrates an implementation of an MLN subnet with low latency.

FIG. 4C illustrates an implementation of an MLN subnet with highthroughput.

FIG. 5A illustrates partitioning a mesh of Tiles to execute differentsubnets.

FIG. 5B illustrates deterministic and non-deterministic phases fordifferent partitions.

FIGS. 6A-6D are block diagrams of meshes in which the Tiles havedifferent capabilities.

FIGS. 7A-7D are block diagrams of meshes with different networktopologies.

FIGS. 8A-8C are block diagrams of meshes with different arrangements ofTile memories.

FIG. 9 is a block diagram of an integrated circuit product that includesan MLA.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The figures and the following description relate to preferredembodiments by way of illustration only. It should be noted that fromthe following discussion, alternative embodiments of the structures andmethods disclosed herein will be readily recognized as viablealternatives that may be employed without departing from the principlesof what is claimed.

FIG. 1A is a block diagram of one example of a system with a machinelearning accelerator (MLA) 170 and corresponding compiler 120, accordingto the invention. The compiler 120 receives a description of a machinelearning network 100 and generates a computer program 150 thatimplements the machine learning network using MLA 170. The computerprogram 150 includes instructions that are executed by processingelements (Tiles) in the MLA according to a schedule determined by thecompiler. For convenience, these will be referred to as staticallyscheduled instructions. The instructions executed by the Tiles (Tileinstructions) are statically scheduled because the compiler candetermine which instructions are executed by which Tiles at what times,as will be explained in greater detail below. For example, for thestatically scheduled instructions, there are no conditions, branching ordata dependencies that can be resolved only at run-time, and which wouldaffect the timing and order of the execution of the instructions. Notethat the static schedule determined by the compiler may or may not beincluded as part of the instructions and computer program. In someembodiments, the computer program may expressly include the schedule,specifying that instruction A is executed at cycle X, instruction B isexecuted at cycle X+4, instruction C is executed at cycle X+12, etc. Inalternate embodiments, the computer program may specify only thatinstruction A is executed, followed by instruction B, and theninstruction C, but without any scheduling information. Even though thestatic schedule is not expressly specified, these instructions willstill execute according to the schedule determined by the compilerbecause the compiler knows how long it takes to execute eachinstruction. As a result of the static scheduling, the MLA andinstruction set for the MLA may be simplified, with the complexityoffloaded to the compiler. A simpler MLA can result in lower cost, lowerpower consumption and higher performance, all of which are desirable forimplementation in edge devices.

In more detail, the MLN 100 may be described by an architecture andparameters. A depiction of an MLN is shown to the right of box 100 inFIG. 1A. Most MLNs include multiple layers 102, each with one or morenodes which are represented by circles in FIG. 1A. The lines betweennodes in FIG. 1A represent interconnections between the nodes (andlayers). Each node calculates a weighted sum of the values received fromits connected nodes, possibly also applying a bias. Examples are matrixmultiplication and convolution. Each node may also apply certainfunctionality (operators), such as nonlinear functions (e.g., tanhfunction), softmax operator, etc. A typical node may compute an output:

y=F(Σw _(i) x _(i) +b)  (1)

where x_(i) are the inputs received from other nodes i, w_(i) areweights, b is a bias and F( ) is a nonlinear operator. The MLNarchitecture includes the number of nodes (and layers) and theirinterconnectivity, and the operators applied at nodes. The operators maybe described in a parameterized form. The MLN parameters include theweights, biases, and parameters for the operators.

MLNs may vary in size, depending on the desired task. Small MLNs mayhave 5-10 or fewer layers, medium size MLNs may have 30-50 layers, andlarge MLNs may have 100 or more layers. Examples of inputs include text,images and video. Some of the layers may be fully interconnected (i.e.,every node in one layer provides input to every node in the next layer),and others may be more locally interconnected (e.g., to implementconvolutions). Each weighted interconnect represents a scalarmultiplication. The total number of scalar multiplications required toimplement an MLN may be on the order of millions, billions, tens ofbillions or even more. These may be carried out by matrixmultiplications.

The MLA 170 includes a plurality of Tiles 180 and an on-chip memorysystem implemented on a semiconductor die. The Tiles are organized intoone or more meshes of interconnected Tiles. A depiction of a Tile meshis shown to the right of box 170 in FIG. 1A. In each mesh, the Tiles 180are organized in a regular pattern and the interconnections within eachmesh provide data transfer paths between Tiles in the mesh. The Tilesexecute computations according to instructions received by the Tiles andusing data stored in the on-chip memory system. These instructions maybe for computations and/or for data transfer. Computations includemultiply (including matrix multiply), add, and operators (e.g.,nonlinear functions, lookup table, min/max, pooling). These arecomputations that implement the MLN. In the example of FIG. 1A, thecomputations performed by layers 102A-D are allocated to groups 182A-Dof Tiles as indicated. The allocation is not required to be 1:1. Forexample, multiple layers could be allocated to a single Tile or viceversa. Not every computation required to implement an MLN need beexecuted by a Tile; some computation may be executed outside the MLA(e.g., floating point operations, if the Tiles only do integerarithmetic). Tiles typically will at least perform matrixmultiplication.

The compiler 120 receives a description of the MLN 100 and generates acomputer program 150 that implements the MLN using the MLA 170. Thecomputer program 150 receives an input sample for the MLN and executesthe operations of the MLN to produce the output for the MLN. Thecomputer program 150 includes instructions to be executed by the Tilesfor implementing computations in the MLN and may also includeinstructions to be executed by other elements, such as a controlleroutside the Tiles.

As shown in FIG. 1B, the compiler partitions the Tile instructions intoone or more deterministic phases 152A,B,C which typically utilizemultiple Tiles. The instructions in a deterministic phase 152 may bestatically scheduled by the compiler. For example, a deterministic phase152 may include a series of computations required to implement a portionof the MLN, where the time required for each computation and associateddata transfers is known. As a result, the compiler may staticallyschedule the Tile instructions within that deterministic phase relativeto the other Tile instructions in the phase. The resulting computerprogram produced by the compiler then implements an allocation ofinstructions to Tiles and a schedule for executing the instructions asdetermined by the compiler, although these may not be expresslycontained within the computer program. In the example of FIG. 1A, thecomputations performed by layers 102A-D are allocated to groups 182A-Dof Tiles as indicated. In addition, all of the Tile instructions(including both for computation and for data transfer) are executed in asingle deterministic phase.

The computer program may also include non-deterministic phases 154X,Y.For example, non-deterministic phases 154 may include data fetch orinstruction fetch from off-chip memory where the time required toexecute the operation varies too much to allow reliable synchronizationwith other operations. Other examples include computations that occuroff-chip, and conditions, branching and other programmatic constructsthat depend on values not known until run-time. The breaks in therectangles for the non-deterministic phases 154 indicate that the timingis not deterministic, whereas the deterministic phases 152 arerepresented by rectangles without breaks. In FIG. 1B, the deterministicand non-deterministic phases are shown as alternating. This is notrequired. For example, deterministic and non-deterministic phases mayexecute concurrently.

FIG. 1B also shows more detail of deterministic phase 152B, which showsthe static schedule computed by the compiler for executing Tileinstructions in this phase. The phase 152B begins at some time when allof the Tiles are synchronized, which for convenience is marked as cyclec0 in FIG. 1B. The Tiles may have circuitry that synchronizes the Tiles.For example, each Tile may monitor when it is ready to begin executionof a deterministic phase 152B and then actual execution begins when allTiles signal that they are ready. Alternatively, an external controllermay synchronize the Tiles and start the deterministic phase 152B whenall Tiles are ready.

In this example, the instructions are executed by three Tiles, asdenoted by T1, T2 and T3. Each Tile has two pipelines: a “D” pipelinefor executing data transfer instructions and a “C” pipeline forexecuting compute instructions. The row labeled T1 D shows instructionsexecuted by the Tile 1 D (data transfer) pipeline, and the row labeledT1 C shows instructions executed by the Tile 1 C (compute) pipeline. Forthis example, assume that all the data transfer instructions areinstructions that load new data into that Tile for consumption by thecompute pipeline. The white regions of each row denote the execution ofinstructions and the hashed regions indicate that the pipeline is idlingor executing a NO-OP (no operation).

For Tile 1, instruction 155 a transfers data into Tile 1 and instruction155 b then performs a computation that consumes that data. Instruction155 b is dependent on instruction 155 a. Here, the T1 C pipeline is notrequired to continuously poll the T1 D pipeline at run-time for when thedata is available, and run-time message passing between the pipelines isnot required to indicate that the data is available. Rather, because theduration (i.e., time required to execute) of instruction 155 a is known,the compiler knows when the data will be available (for convenience,marked as cycle c1 in the figure) and can construct a static schedule inwhich instruction 155 b starts execution then. The duration ofinstruction 155 b is also known, so the compiler knows that computeinstruction 155 d may start after instruction 155 b. In this case, thecompiler determines a static schedule in which instruction 155 d startsat cycle c3. Compute instruction 155 d depends on data brought into theTile by instruction 155 c. The duration of instruction 155 c is known,so the compiler knows that in the static schedule, instruction 155 cmust start at cycle c2 or earlier. This pattern is repeated for pairs ofdata transfer instructions and compute instructions 155 e-f, 155 g-h,155 i-j.

For Tile 2, compute instruction 155 l depends on data from data transferinstruction 155 k. However, instruction 155 k does not start immediatelyat cycle c0. Rather, it has a delayed start at cycle c4. This may bebecause the data transfer path required by instruction 155 k is occupiedby some other data transfer instruction and is not available until cyclec4. The start time of instruction 155 k in the static schedule is notdetermined by run-time arbitration or contention mechanisms for theshared data transfer path. Rather, the compiler knows that the datatransfer path is occupied since the compiler knows the start times anddurations of all the instructions, so the compiler simply creates astatic schedule in which instruction 155 k does not start until cycle c4when the compiler knows the data transfer path will be available.Similarly, data transfer instruction 155 m has a delayed start time.Perhaps the T2 D pipeline is being used to transfer out the results ofcomputation 155 l and does not become available until cycle c5.

For Tile 3, computation 155 n starts immediately at cycle c0. Perhapsthe required data was loaded into Tile 3 during some prior phase. Datatransfer instructions 155 o and 155 p load data for compute instruction155 q. They are separated in time, perhaps because different pieces ofdata were not available or the data transfer paths were not availableuntil those times. As a final example, data transfer instruction 155 rloads data for compute instruction 155 s. In the static schedule, thecompiler places instruction 155 r well in advance of when the data isrequired, but this may be because that is when the data transfer path isavailable or perhaps the data was transferred out of the sourcing Tilein order to make room in that Tile.

Execution of the instructions according to the static schedule atrun-time may be implemented in different ways. In one approach, thecomputer program includes an express schedule for the execution of theinstructions. Continuing the example of FIG. 1B, the computer programmay specify that instruction 155 a executes at cycle c0, instruction 155b at cycle c1, instruction 155 c at cycle c2, etc. Alternatively, thecompiler may fill each instruction stream with NO-OPs to achieve thecorrect timing. A NO-OP (no operation) is an instruction that occupies acertain number of cycles without other activity. For example, thecompiler knows that instruction 155 a will end at cycle c1 andinstruction 155 b is supposed to begin at cycle c1. It may fill thespace between cycles c0 and c1 with NO-OPs for the T1 C pipeline. The T1C pipeline then just continuously executes instructions from its queue,and the NO-OPs ensure that instruction 155 b is executed according tothe compiler's static schedule. In yet another approach, the staticschedule may be implemented by hardware. The T1 C pipeline may juststall on the execution of instruction 155 b until the data frominstruction 155 a is ready. The compiler knows that data will be readyat cycle c1 and, therefore, instruction 155 b will execute starting atcycle c1 even though the Tiles are unaware of the static schedule.Regardless of the implementation, for convenience, all of thesesituations will be described using the phrase “static schedule.” Thus, astatement that the compiler statically schedules the instructions isintended to include all of the above implementations and is not meant toimply that the computer program expressly includes a scheduled time foreach instruction.

In order to statically schedule the instructions in a deterministicphase, the compiler typically will know the duration of each instruction(i.e., how long each instruction takes to execute), the capabilities ofeach Tile (which Tiles can execute which instructions), the topology ofdata transfer paths to and from Tiles (including between Tiles, andbetween Tiles and on-chip memory), and the computations required andtheir dependencies (i.e., the MLN description). With this information,the compiler can schedule unconditional start times for the Tileinstructions. Here, unconditional refers to run-time conditions. Theexecution order of statically scheduled instructions will not change asa result of run-time conditions, branching or dependence on inputvalues. As a result, compute instructions may be scheduled for starttimes when all of the required data for the computation is known to beavailable and the compute pipeline is also known to be available. Theneed for run-time determination of whether data has arrived and whetherthe compute pipeline is available may be avoided. Analogously, datatransfer instructions may be scheduled for start times when the datatransfer path is known to be available. The need for circuitry to handlearbitrations, or to check for or resolve contentions and collisions onshared data transfer paths at run-time may be avoided. The need forrouting tables and other circuitry to determine routing at run-time mayalso be avoided. FIGS. 4 and 5 provide further examples of how thecompiler converts a description of an MLN to a deterministic phase ofstatically scheduled instructions executed by the Tiles.

Other aspects include components, devices, systems, improvements,methods, processes, applications, computer readable mediums, and othertechnologies related to any of the above.

FIGS. 2-3 are more detailed descriptions of an example system thatincludes an MLA and corresponding compiler. FIG. 2 shows the hardwarecomponent and FIG. 3 shows the software development environment.

FIG. 2A is a block diagram of a hardware system including an MLA 270.The MLA 270 includes all the components shown in FIG. 2A, except theoff-chip L3 memory 290. The MLA components are implemented on a singledie as part of a single chip. The MLA 270 includes one or more mosaics272A-N. In this example, all of the mosaics are the same. Each mosaic272 includes a mesh of Tiles 280, an on-chip memory system and acontroller 277. In FIG. 2A, the on-chip memory system is a multi-levelmemory system, which includes a level 1 (L1) memory distributed amongthe Tiles (see FIG. 2B) and a level 2 (L2) memory 274 shared by theTiles. If there are multiple mosaics 272, the MLA 270 may include adedicated interconnect 279 for connecting the different mosaics. Eachmosaic also includes an interface 278 to the interconnect 279.

FIG. 2B is a block diagram of a Tile 280 within the MLA. In thisexample, all the Tiles are the same. Each Tile 280 includes an L1 memory282. Each Tile 280 also includes a data transfer pipeline that executesinstructions for transferring data to and from the L1 memory 282. Here,the Tiles 280 are arranged in a rectangular array as shown in FIG. 2A,with each Tile connected to its adjacent neighbors. Interior Tiles areconnected to four adjacent Tiles. Edge Tiles are connected to adjacentTiles and also to L2 memory 274. In FIG. 2B, the L1 memory 282 mayreceive data from any of its adjacent Tiles and/or from L2 memory if itis an edge Tile. Similarly, it may transfer data to any of its adjacentTiles and/or to L2 memory if it is an edge Tile. The data transferoperations are controlled by data transfer instructions received andexecuted by the Tiles.

Each Tile 280 also includes a compute pipeline 285 for executingcomputations using data stored in the L1 memory 282. The L1 memory actsas software-configurable registers for the compute pipeline 285. Thecompute pipeline 285 includes matrix multiplication circuitry 286, suchas a systolic array, and circuitry for implementing different types ofoperators 287. The computations are controlled by compute instructionsreceived and executed by the Tiles.

In this particular example, all of the data transfer instructions andcompute instructions executed by the Tiles are statically scheduled.These instructions include data transfer between L1 memories indifferent Tiles, and data transfer between L1 memory and L2 memory. Datatransfer instructions may specify one hop at a time (e.g., transfer datato the east neighbor Tile) or may specify destination and path throughintermediate Tiles (e.g., transfer data to Tile (5,5) using patheast-east-north-north-east). The instructions also include matrixmultiplies performed by the Tiles and operators applied by the Tiles.These operations do not require very many different instructions toimplement, so the overall instruction set may be fairly small, forexample not more than 20 instructions, or not more than 50 instructions.

The L3 memory 290 is off-chip. In this example, the L1 and L2 memoriesare implemented as on-chip SRAM and the L3 memory is implemented as DRAM(flash memory and SSD drives are other alternatives). Because the L1 andL2 memories are implemented as SRAM, the data transfers between L1memories or between L1 and L2 memories have deterministic timing, sothese data transfer instructions can be statically scheduled by thecompiler. However, data transfer from off-chip DRAM is moreunpredictable in timing. As a result, these instructions arenon-deterministic in nature and they are executed by the microcontroller277. Therefore, they are executed in one of the non-deterministic phasesand they are not statically scheduled.

In one approach, the instructions in the computer program and the datarequired for computation (e.g., input, weights, biases, parameters foroperators) are initially loaded into L3 memory 280. From time to time,instructions and associated data are transferred from L3 memory intoL1/L2 memory during a non-deterministic phase since the timing of datatransfers from DRAM is not deterministic. Once these instructions anddata are loaded into L1/L2 memory, the computer program enters acorresponding deterministic phase in which the Tiles execute the loadedinstructions according to a static schedule. The non-deterministic anddeterministic phases may occur concurrently. For example, data may becontinuously streamed into the L1/L2 memory during the non-deterministicphase, with the corresponding statically scheduled instructions from thedeterministic phase consuming that data. In one approach, the Tilesexecute only statically scheduled instructions, and all non-staticallyscheduled instructions are executed by processing elements outside theTile mesh, for example, the microcontroller 277.

SRAM has predictable timing so implementing the L1 and L2 memories asSRAM allows the compiler to statically schedule data transfers fromthose memories into the Tiles for computation. However, there is a limitto the amount of SRAM that may be implemented on a die. In order toincrease the effective size of SRAM, a virtual SRAM approach may beused. In one approach, the compute instructions that consume certaindata are not fetched into the Tiles until after the corresponding datahave been transferred from DRAM (L3 memory) to SRAM (L1/L2 memory). Thisguarantees that the compute instructions will not be executed by theTiles before the data is available. All data effectively will appear asif it is transferred to the Tiles from SRAM for computation, even if allof the data would not fit into the available SRAM.

L2 memory may also be used to temporarily store interim values that aretoo voluminous to store in L1 memory. For example, a layer K of the MLNmay produce a large amount of data at its output, to be used as input tothe next layer K+1. The layer K output may be stored in L2 memory andthen retrieved from L2 memory as needed for the next layer'scomputations. This may be implemented using a ping pong buffer approachwhen multiple input samples are processed as a pipeline. The L2 memoryis divided into two regions A and B. When a first input sample isprocessed, the layer K output is stored in region A of the L2 memory.The computations for layer K+1 retrieve the stored values from region A.At the same time, the second input sample is processed and the layer Koutput is stored in region B of the L2 memory. The two regions thenalternate, with the Tiles implementing layer K storing to one regionwhile the Tiles implementing layer K+1 read from the other region. Thesynchronization is implemented by the static scheduling. The compilerknows when regions AB will be ready and the instructions to implementlayer K+1 will execute after that time. No synchronization primitivesare needed.

FIG. 3 is a block diagram of a software development environmentincluding an ML compiler 320. In this example, the software developmentenvironment also includes a model optimizer 330. The model optimizer 330receives a description of the MLN 300 and produces an optimized graph335 of the MLN. It may apply optimizations such as quantization 331,pruning 332 and/or compression 333. Quantization 331 reduces theresolution of calculated values. For example, floating point values maybe quantized to a certain number of bits and then integer math usedinstead of floating point math. This reduces the complexity and powerconsumed by the Tiles. Pruning 332 removes parts of the MLN that do notcontribute significantly to the overall results. For example, if certainweights are zero or close to zero, those weighted interconnects may bepruned. Finally, because MLNs contain a large amount of data,compression may be used successfully to reduce data transfer bandwidths.

The resulting optimized description 335 of the MLN may be expressed as agraph, in which the nodes of the graph represent nodes in the MLN andthe edges of the graph represent the weighted interconnects. Thecompiler 320 receives the optimized graph 335 and produces the resultingcomputer program 350. The compiler 320 may perform operations includingstatic scheduling 322, PPA (power performance area) optimizations 324,graph optimizations 326 and/or partitioning 328. Static scheduling 322of the appropriate instructions was described above.

PPA optimization 324 includes different optimizations of the computerprogram 350. For example, the allocation of MLN computations to Tilesmay be optimized to reduce power consumption, to increase performance(such as reducing latency or increasing throughput) and/or to reducearea (e.g., number of Tiles used). Examples of this are described inFIG. 4.

For a given graph representation of an MLN, the number of computationsrequired to execute the MLN is fixed. As a result, in one approach, thecompiler may optimize to increase the utilization of compute resourcesin the Tiles—to keep the compute pipelines as busy as possible. However,for a Tile to execute a computation, the data for that computation mustbe available. This means that any prior computations must be completedand that those results must be transferred to the Tile doing the nextcomputation. Thus, rather than focusing on computations, the compilermay optimize with respect to data transfer to reduce the wait times ofcomputations. It may also allocate computations to Tiles in order toreduce data transfers between Tiles in the same mesh, to reduce datatransfers from outside the MLA and/or to reduce data transfers thatcross the boundary of the mesh (e.g., reducing data transfers between L1and L2 memory and trying to keep all data in L1 memory).

The compiler 320 may also optimize 324 the computer program 350, subjectto constraints on power, performance, area and/or any of the quantitiesdescribed above. Graph optimization 326 includes analysis of the graphrepresenting the MLN to prune, merge or quantize links, parameters,values, and layers to achieve better performance. Partitioning 328concerns mapping the computations in the MLN to an implementation on theMLA. This includes determining which computations are allocated to whichTiles and how data flows through the mesh of Tiles during computation.If there are multiple mosaics, it also includes determining whichcomputations are allocated to which mosaics.

The resulting computer program 350 may be loaded into memory forexecution on a machine learning accelerator 370. For example, onepossible application is object detection. In this case, the inputs areimages captured by a video camera. The MLN 300 has been trained toidentify certain objects in the video images. The computer program 350implementing the MLN is loaded onto memory that is accessible by the MLA370, which is implemented as a chip inside the camera. This way, imagescaptured by the video camera may be immediately analyzed by the computerprogram 350 running on the MLA 370.

In addition to the MLA 370, the computer program 350 or parts of it maybe run on a software simulator 336 and/or hardware emulator 338(including FPGAs configured as MLAs). These may be used for productdevelopment, debugging and/or prototyping. For some purposes, a fullsimulation or emulation is not necessary. For example, to check thatthere are no collisions or conflicts between statically scheduledinstructions, only the flow of data may be simulated or emulated. It isnot necessary to compute actual values.

FIGS. 4A-4C illustrate three different computer programs forimplementing a portion of an MLN (a subnet) on a set of Tiles. Eachexample implements the subnet

Y=F(W1X1+W2X2+W3X3+W4X4)  (2)

where Xn are matrices computed by prior nodes, Wn are correspondingweights, and F( ) is a non-linear operator. Pn are intermediateproducts. The implementation in FIG. 4A utilizes a low number of Tiles,the implementation in FIG. 4B has low latency, and the implementation inFIG. 4C has high throughput. In all of these figures, each row shows theinstructions executed by a different Tile. Each column shows a differenttime period, where time is designated in cycles C. For purposes of thisexample, matrix multiplications are assumed to take 8 cycles, Tile toTile transfers take 2 cycles, and all other instructions take 4 cycles.

In FIG. 4A, the compiler creates a computer program in which all of theinstructions are executed by a single Tile 1. In cycles 1-4, Tile 1loads the data for matrices W1 and X1 into its local memory (e.g., L1memory). In cycles 5-12, Tile 1 executes the instruction to matrixmultiply W1 times X1 and the result is stored in local memory for Tile1. In cycles 13-24, Tile 1 repeats this process for W2 and X2. In cycles25-28, Tile 1 adds the two partial products P1 and P2. This processrepeats in cycles 29-60, at which point Tile 1 has computed the sum ofall the matrix multiplications Wn times Xn. In cycles 61-64, Tile 1executes the instruction to apply the nonlinear operator F. In cycles65-68, the result Y is stored. Only one Tile is used to complete thecomputation, but the computation takes 68 cycles to complete.

In FIG. 4B, the computer program calculates Y with minimum latency,completing the computation in 32 cycles. In cycles 1-12, the partialproducts of Wn times Xn are computed for all of the n, using fourseparate Tiles. These partial products are then summed, but thisrequires some Tile-to-Tile data transfer. In cycles 13-14, the partialproduct P1 is transferred from Tile 1 to Tile 2. This is indicated bythe Move command for Tile 1. [P1 available] for Tile 2 indicates thatTile 2 receives P1 as a result of the Move command executed by Tile 1.P1 is then summed with P2 by Tile 2 in cycles 15-18. In parallel duringthe same cycles, Tiles 3 and 4 sum P3+P4. This repeated in cycles 19-24to sum the two partial sums P5+P6. Tile 4 applies the operator F( ) andthen stores the result in cycles 25-32. The additional Tiles allow forhardware parallelism, particularly in computing the matrix multiplies.This shortens the total computation time from 68 cycles to 32 cycles.

In FIG. 4C, the computer program calculates Y in a manner that takesadvantage of the hardware parallelism of FIG. 4B, but with higherthroughput. Each Tile is responsible for one computation, with theresult then transferred to other Tiles to perform other computations.Cycles 1-12 are the same as in FIG. 4B. Tiles 1-4 compute the partialproducts of Wn times Xn. However, these partial products P1-P4 are thentransferred to Tiles 5-6, which sum P1+P2 and P3+P4, respectively, incycles 13-18. These results are transferred to Tile 7, which sums themin cycles 19-24. The total sum P7 is transferred to Tile 8, whichapplies the operator F( ) in cycles 25-30. The overall computation takessomewhat longer than FIG. 4B (34 cycles versus 32 cycles) because thisimplementation has an extra Tile-to-Tile data transfer. However, a newinput sample may be started in Tiles 1-4 after cycle 14, whereas a newinput sample may not be started in Tiles 1-4 of FIG. 4B until aftercycle 32.

FIG. 4 provided different examples of how a particular MLN subnet may beallocated to Tiles. FIGS. 5A and 5B illustrate partitioning the mesh ofTiles to different subnets. In FIG. 5A, the MLA includes a 6x6 mesh(element 580 in FIG. 5A) of Tiles. From time t0 to t1, the mesh 580 isutilized to implement two different MLNs: MLN A and MLN B. The Tiles aredivided into three partitions 582A, 582B1 and 582B2. Partition 582Aimplements MLN A, partition 582B1 implements the first 10 layers of MLNB, and partition 582B2 implements the remaining 15 layers of MLN B. MLNB may be partitioned in this manner because some off-Tile operations maybe required between layers 10 and 11. Maybe the output of layer 10requires a computation that is performed off-Tile in a non-deterministicmanner, or maybe layers 11-25 require data that cannot be loaded in amanner consistent with the static scheduling of layers 1-10. After timet1, the mesh 580 continues to implement MLN B using partition 582A, butMLN A is replaced by MLNs C and D using partitions 582C and 582Drespectively.

FIG. 5A shows a progression over time. The front diagram shows thepartitioning at an earlier time and the subsequent diagrams show thepartitioning at later times. The times are indicated to the lower rightof the diagrams. At time t0, the mesh is partitioned so that the bottom2×6 Tiles implement MLN A, the upper left 4×3 Tiles implement MLN Blayers 1-10, and the upper right 4×3 Tiles implement MLN B layers 11-25.At time t1, MLN B is no longer required and is replaced by MLNs C and D.The upper left 4×2 Tiles now implement MLN C, and the upper right 4×4Tiles now implement MLN D.

Note that each of these partitions may run deterministic andnon-deterministic phases separately from each other. Partition 582Aimplements MLN A, which is independent of MLN B implemented bypartitions 582B1 and 582B2. Thus, partition 582A may run separately fromthe other two partitions. At time t1, partition 582A may continue torun, unaffected by the change from MLN B to MLNs C and D.

FIG. 5B illustrates deterministic and non-deterministic phases forpartitions 582B1 and 582B2. The format is similar to FIG. 1B. Each rowrepresents different phases of instructions. The white regions of eachrow denote the execution of instructions and the hashed regions indicateidling. Non-deterministic phases are indicated by breaks in therectangles. From top to bottom, the rows are the following. The top rowhas instructions to load data for the computations of layers 1-10 fromDRAM into the MLA. This data will be consumed by Tile partition 582B1.Referring to FIG. 2A, this is performed by the controller 277 and thesephases 555 are non-deterministic because they are loads from DRAM. Thesecond row has the deterministic phases 556 of statically scheduled Tileinstructions that implement the computations for layers 1-10. Similarly,the fourth row has non-deterministic phases 558 for loading data for thecomputation of layers 11-25, and the bottom row has the deterministicphases 559 of statically scheduled Tile instructions that implementthese computations, respectively. The middle row has othernon-deterministic instructions 557. In this example, this includesnon-deterministic computations 557 a-c that occur outside the MLA, andinstructions 557 d to repartition the MLA.

The suffixes indicate different input samples. The phases that end in -aapply the MLN to one input sample, the phases that end in -b apply theMLN to the next input sample, etc. The arrows indicate dependencies.Consider first input sample a. A controller loads 555 a the relevantdata (input values, weights, biases, operator parameters) from DRAM intothe MLA memory. After this is completed, the Tiles 582B1 may performtheir computations 556 a using this data. The Tile output is transferredoff-chip for a computation 557 a that is not performed by the Tiles. Inthe meantime, the controller loads 558 a the relevant data for layers11-25. When data from both non-deterministic phases 557 a and 558 a areavailable, Tile partition 582B2 performs its computations 559 a. TheTile computations within each phase 556 a and 559 a are staticallyscheduled within their respective non-deterministic phases, but the timebetween phases 556 a and 559 a may vary. The processing of input samplesb and c have the same dependencies and general flow.

At some point (time t1 in FIG. 5A), the controller ends execution of MLNB and starts execution of MLNs C and D. The compiler has provideddifferent schedules with different partitions of the Tiles as partition582C for MLN C and partition 582D for MLN D. This is phase 557 d. Theprocess then continues with each of the active partitions 582A, C, Dexecution instructions to implement their respective MLNs.

The approach based on static scheduling described above is notrestricted to the examples described above. For example, differentnetwork topologies of Tiles may be used. FIGS. 6-8 illustrate some otherexamples of different Tile meshes which may also be staticallyscheduled, so long as the time required to execute computations and totransfer data between Tiles is deterministic and may be determined atcompile time.

In the previous example of FIG. 2, the Tiles in the mesh all had thesame capability. Each Tile had a compute pipeline that could performmatrix multiplication and implement certain operators. Each Tile alsohad a data transfer pipeline to transfer data to and from its local L1memory. This was just an example. FIGS. 6A-6D are block diagrams ofmeshes in which the Tiles have different capabilities.

In the mesh of FIG. 6A, there are two different types of Tiles labeledC1 and C2. The C1 Tiles have one type of compute capability and the C2Tiles have a different type of compute capability. For example, the C1Tiles may be limited to matrix multiplication while the C2 Tiles applycertain operators (i.e., the different stage of the compute pipeline 285in FIG. 2B is split into separate Tiles). In FIG. 6A, the Tiles arearranged in columns which alternate between the C1 and C2 capabilities.This may be a good layout for data flowing left to right, since it iscommon for MLNs to have matrix multiplications followed by operators. Inthe mesh of FIG. 6B, there are three types of tiles C1, C2 and C3. TheTiles are arranged in diagonals, so that no Tile is adjacent to anotherTile with the same capability.

FIG. 6C shows a regular array of Tiles C with some Tiles that havespecial compute capability C1-C4. In this example mesh, the specialTiles replace some of the regular Tiles C in the array. The Tiles arearranged in clusters 610A-D as indicated by the dashed lines. Eachspecial Tile is surrounded by eight regular Tiles. The mesh of FIG. 6Dis similar to FIG. 6C, except that the special compute Tiles C1-C4 arereplaced by Tiles D that only perform data transfer. These Tiles D actas instruction-driven switches to allow data transfer between any of theeight surrounding compute Tiles C in the cluster 620. Additionaldiagonal interconnects are added to allow data transfer between Tiles Dand the corner Tiles C of each cluster 620.

FIGS. 7A-7D are block diagrams of meshes with different networktopologies. In these figures, an arrow between two Tiles indicates adata transfer path between the two Tiles. Arrows which touch only oneTile indicate a data transfer path between that Tile and L2 memory. Thearrowheads indicate the direction of data transfer, whetherunidirectional or bidirectional. In FIG. 2A, the networkinterconnectivity of the Tiles was based on a rectangular array. EachTile was connected to its immediate neighbors. The edge Tiles were alsoconnected to L2 memory. This was just an example. Tiles could beconnected to different numbers of other Tiles, for example. Otherexamples are described below.

The mesh in FIG. 7A has the same network topology as in FIG. 2A, exceptthat the edge Tiles are also connected to each other. Thus, data may betransferred between the leftmost and rightmost Tiles in any row andbetween the top and bottom Tiles in any column. In this example, alldata links are bidirectional. The mesh in FIG. 7B has the same networktopology as in FIG. 2A, except that all of the data links areunidirectional. Data has a preferred direction of flow from the top andleft, to the bottom and right. This reduces the number of data transferpaths by half.

In FIG. 7C, the preferred direction of data flow is along two paralleltracks 710A and 710B, which snake from the upper left to the lowerright. Every so often there are bridges 712 to allow data transferbetween the two tracks 710.

The network topology of FIG. 7D is based on clusters 720. For eachcluster 720, a set of edge Tiles may receive data from L2 memory andanother set of edge Tiles may transmit data to L2 memory. The Tiles inthe cluster are arranged in a ring in a direction from the Tilesreceiving data from L2 memory to the Tiles transmitting data to L2memory. For example, in cluster 720A, the top three Tiles can receivedata from L2 memory and the left three Tiles can transmit data to L2memory. The eight outer Tiles in the cluster form a ring with data flowin the clockwise direction. Thus, data that enters the cluster from L2memory may be processed by the Tiles in the ring before exiting thecluster back to L2 memory. The center Tile C may transfer data to andfrom any of the corner Tiles in the cluster. It may also transmit dataaround a higher level ring to the other center Tiles of the otherclusters 720.

FIGS. 8A-8C are block diagrams of meshes with different arrangements ofTile memories. Tile memories are the local memory accessed by the Tiles(e.g., L1 memory in FIG. 2A). In these figures, the rectangle Mrepresents a Tile memory and the circle C represents a Tile computecapability. The lines between M's are data transfer paths betweenmemories. The lines between M and C indicate that compute capability Cloads and stores data from that memory for its computation.

FIG. 8A shows the architecture described previously in FIG. 2. Each Tilememory M may transfer data to its adjacent neighbor. (In FIG. 2B, datamay be transferred from L1 memory 282 to the L1 memory of an adjacentTile.) Each of the compute capabilities C uses data from a dedicatedlocal memory M. (In FIG. 2B, the compute pipeline 285 reads operandsfrom L1 memory 282 and writes the result of its computation to L1 memory282).

In FIG. 8B, the memories M are connected to each other in the samenetwork as FIG. 8A. However, the compute capabilities C are eachconnected to two neighboring memories M. As a result, a compute pipelineC may read operands from one memory M, perform a computation and writethe result to the adjacent memory. This saves a step of data transferbetween the two memories.

In FIG. 8C, multiple compute capabilities C share a memory M. This isanother way to transfer the result computed by one pipeline C to theinput of another compute pipeline, without requiring an explicit datatransfer between separate memories.

FIGS. 6-8 show some examples of different architectures for the mesh ofTiles. Other variations will be apparent. For example, the meshes couldbe irregular rather than a regular pattern. Furthermore, the mesharchitecture could be instruction-driven or hardware configurable. Forexample, assume that all Tiles have capabilities C1, C2 and C3. TheTiles may be hardware configurable (like FPGAs) so that, if thearchitecture of FIG. 6A or 6B was desired, the Tiles could be configuredfor the specific capability C1, C2 or C3. Alternatively, the Tiles maybe configured for capabilities C1, C2 or C3 by software instructions inthe computer program. The unutilized capabilities may be idled, powereddown or even disconnected (either temporarily or permanently). The sameis true for the network topology and the memory architecture.

FIG. 9 is a block diagram of an integrated circuit that includes an MLA970. In other words, other components may be included on the same die asthe MLA. This example includes the following additional blocks:application processor 910 (e.g., general purpose CPU runningapplications), computer vision processor 912 (or other types ofapplication-specific processors), safety 914, security 916, additionalSRAM (memory) 920 and input/output circuitry 922. It also includes anetwork 930 for communication between the different components. Thistype of semiconductor chip may be referred to as a system-on-chip (SoC).

The connections to the external world include camera inputs 940 for thecomputer vision processors, ports for debug 942 and configuration 944, aconnection 946 to external memory (e.g., DRAM), chip-to-chip connections948, and network connections 950 (e.g., Ethernet and PCIe).

The SoC of FIG. 9 may be combined with other components to performvarious tasks in edge devices. Example applications for edge devicesinclude automotive and other forms of transportation includingautonomous transportation, agricultural, industrial, robotics, drones,surveillance and security, smart environments including smart cities,medical and personalized health. Example tasks include computer vision,image analysis, image understanding, speech recognition, audio analysis,audio understanding, natural language processing, classification andpattern recognition tasks. For edge devices, it may be desirable toperform certain tasks in real-time.

In addition to memory and other programmable processors, an edge devicemay also include sensors, such as cameras (both still image and videocameras), microphones, temperature sensors, pressure sensors and othertypes of sensors. The sensors may capture samples that are used asinputs to a computing pipeline within the edge device. For example,image samples may be input to the computer vision processors 912, whichperform initial operations such as edge detection and enhancement,contrast enhancement, motion detection, and optical flow. Raw and/orprocessed images may be then input to the MLA 970 for analysis by themachine learning network. The MLA may also receive other inputs, such asmetadata from other sources and data from other sensors. The applicationprocessors 910 may also perform various functions in the overallpipeline and may also serve as a master controller that coordinatesoperation of the MLA and the other programmable processors in thepipeline.

Edge devices may be portable with less power available for computationscompared to, for example, cloud-based server farms. It may also bedesirable for the computing pipeline within the edge device to performtasks without utilizing cloud-based or other remote compute resources.In some implementations, the MLA implements computations in the machinelearning network at a performance of at least 50 TOPs (50 trillionoperations per second) at a power consumption of not more than 5 watts.The performance may be increased by increasing the number of Tiles inthe mesh or the number of Tile meshes on the die.

Although the detailed description contains many specifics, these shouldnot be construed as limiting the scope of the invention but merely asillustrating different examples. It should be appreciated that the scopeof the disclosure includes other embodiments not discussed in detailabove. Various other modifications, changes and variations which will beapparent to those skilled in the art may be made in the arrangement,operation and details of the method and apparatus disclosed hereinwithout departing from the spirit and scope as defined in the appendedclaims. Therefore, the scope of the invention should be determined bythe appended claims and their legal equivalents.

What is claimed is:
 1. A system comprising a machine learningaccelerator (MLA) implemented on a semiconductor die, the MLAcomprising: an on-chip memory system configured to store data used incomputations for implementing a machine learning network; and a mesh ofinterconnected Tiles coupled to the on-chip memory system, the Tilesconfigured to execute one or more deterministic phases of Tileinstructions, each deterministic phase utilizing one or more Tiles andat least one deterministic phase utilizing multiple Tiles, wherein theTiles are configured to execute Tile instructions in the samedeterministic phase in synchronization and the Tile instructions withineach deterministic phase are statically scheduled relative to the otherTile instructions in the same deterministic phase, the Tile instructionsimplementing computations in the machine learning network using datastored in the on-chip memory system.
 2. The system of claim 1 whereinthe deterministic phase includes Tile instructions for data transfer toand from the Tiles, and the Tiles are configured to execute these Tiledata transfer instructions without circuitry for performing run-timearbitration for data transfer paths to and from the Tiles.
 3. The systemof claim 1 wherein the deterministic phase includes Tile instructionsfor data transfer to and from the Tiles, and the Tiles are configured toexecute these Tile data transfer instructions without circuitry forrun-time checking or resolving contentions or collisions on datatransfer paths to and from the Tiles.
 4. The system of claim 1 whereinthe deterministic phase includes Tile instructions for data transfer toand from the Tiles, and the Tiles are configured to execute these Tiledata transfer instructions without circuitry for run-time routing forthese data transfers.
 5. The system of claim 1 wherein the deterministicphase includes Tile instructions that implement the computations in themachine learning network on compute pipelines within the Tiles, and theTiles are configured to execute these Tile data transfer instructionswithout circuitry for determining run-time availability of the computepipelines.
 6. The system of claim 1 wherein the Tiles are configured toexecute the Tile instructions in each deterministic phase, withoutcircuitry for determining run-time conditions or availability of Tilesources used to execute the Tile instructions.
 7. The system of claim 1wherein the MLA further comprises: circuitry external to the mesh ofTiles but coupled to the mesh of Tiles; the circuitry configured to, foreach deterministic phase, synchronize the Tiles utilized in thatdeterministic phase at a beginning of that deterministic phase.
 8. Thesystem of claim 1 wherein the MLA further comprises: circuitry withinthe mesh of Tiles configured to, for each deterministic phase,synchronize the Tiles utilized in that deterministic phase at abeginning of that deterministic phase.
 9. The system of claim 1 whereinthe on-chip memory system is a multi-level memory system that includesL1 memory, L2 memory and data transfer paths between the L1 memories andbetween the L1 and L2 memories; and the deterministic phase includesTile instructions for data transfer between the L1 memories and betweenthe L1 and L2 memories.
 10. The system of claim 9 wherein only Tiles onan edge of the mesh have direct access to L2 memory.
 11. The system ofclaim 1 further comprising: an off-chip memory that is not on the samedie as the on-chip memory system; and a controller external to the meshof Tiles, the controller configured to transfer data between theoff-chip memory and the on-chip memory system.
 12. The system of claim11 wherein: the deterministic phase includes Tile instructions for datatransfer within the on-chip memory system; and the computer programcomprises a non-deterministic phase that includes instructions executedby the controller for data transfer between the off-chip memory and theon-chip memory system.
 13. The system of claim 11 wherein the on-chipmemory system comprises SRAM and the off-chip memory comprises DRAM. 14.The system of claim 1 wherein the Tile instructions include instructionsto configure the on-chip memory system as registers for Tiles thatperform computations.
 15. The system of claim 1 wherein the MLAcomprises two or more meshes of interconnected Tiles and furthercomprises an on-chip interconnect that provides data transfer betweenthe different meshes.
 16. The system of claim 1 wherein the Tiles haveparallel pipelines for execution of Tile data transfer instructions andTile compute instructions.
 17. The system of claim 1 wherein the mesh ofTiles includes at least two different types of Tiles.
 18. The system ofclaim 17 wherein the mesh of Tiles includes Tiles that perform only datatransfer by executing Tile instructions and do not perform computations.19. The system of claim 1 wherein data transfer paths between Tiles inthe mesh establish a preferred direction for data flow through theTiles.
 20. A system for implementing a machine learning network, thesystem comprising: a machine learning accelerator (MLA) implemented on asemiconductor die, the MLA comprising a mesh of interconnected Tiles andan on-chip memory system implemented on the semiconductor die, the Tilesconfigured to execute one or more deterministic phases of Tileinstructions, each deterministic phase utilizing one or more Tiles andat least one deterministic phase utilizing multiple Tiles, wherein theTiles are configured to execute Tile instructions in the samedeterministic phase in synchronization and the Tile instructions withineach deterministic phase are statically scheduled relative to the otherTile instructions in the same deterministic phase, the Tile instructionsimplementing computations in the machine learning network using datastored in the on-chip memory system; and a compiler that receives adescription of the machine learning network and generates a computerprogram that implements the machine learning network on the MLA; whereinthe computer program comprises the deterministic phases of Tileinstructions executed by the Tiles.